HLTCOE JHU Submission to the Voice Privacy Challenge 2024
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914950994198528 |
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| author | Xinyuan, Henry Li Cai, Zexin Garg, Ashi Duh, Kevin García-Perera, Leibny Paola Khudanpur, Sanjeev Andrews, Nicholas Wiesner, Matthew |
| author_facet | Xinyuan, Henry Li Cai, Zexin Garg, Ashi Duh, Kevin García-Perera, Leibny Paola Khudanpur, Sanjeev Andrews, Nicholas Wiesner, Matthew |
| contents | We present a number of systems for the Voice Privacy Challenge, including voice conversion based systems such as the kNN-VC method and the WavLM voice Conversion method, and text-to-speech (TTS) based systems including Whisper-VITS. We found that while voice conversion systems better preserve emotional content, they struggle to conceal speaker identity in semi-white-box attack scenarios; conversely, TTS methods perform better at anonymization and worse at emotion preservation. Finally, we propose a random admixture system which seeks to balance out the strengths and weaknesses of the two category of systems, achieving a strong EER of over 40% while maintaining UAR at a respectable 47%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_08913 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | HLTCOE JHU Submission to the Voice Privacy Challenge 2024 Xinyuan, Henry Li Cai, Zexin Garg, Ashi Duh, Kevin García-Perera, Leibny Paola Khudanpur, Sanjeev Andrews, Nicholas Wiesner, Matthew Audio and Speech Processing Machine Learning We present a number of systems for the Voice Privacy Challenge, including voice conversion based systems such as the kNN-VC method and the WavLM voice Conversion method, and text-to-speech (TTS) based systems including Whisper-VITS. We found that while voice conversion systems better preserve emotional content, they struggle to conceal speaker identity in semi-white-box attack scenarios; conversely, TTS methods perform better at anonymization and worse at emotion preservation. Finally, we propose a random admixture system which seeks to balance out the strengths and weaknesses of the two category of systems, achieving a strong EER of over 40% while maintaining UAR at a respectable 47%. |
| title | HLTCOE JHU Submission to the Voice Privacy Challenge 2024 |
| topic | Audio and Speech Processing Machine Learning |
| url | https://arxiv.org/abs/2409.08913 |